license: apache-2.0
library_name: pytorch
tags:
- object-detection
- computer-vision
- discrete-diffusion
- wacv-2027
datasets:
- detection-datasets/coco
DDM-Det: Fast Object Detection via One-Step Discrete Diffusion
Official pretrained model checkpoint for Fast Object Detection via One-Step Discrete Diffusion, accepted at WACV 2027.
DDM-Det formulates object detection as discrete token generation and performs detection in a single reverse diffusion step.
Results
Evaluated on COCO 2017 validation using a ResNet-50 backbone.
| AP | AP50 | AP75 | FPS |
|---|---|---|---|
| 40.50 | 60.58 | 42.29 | 58.82 |
FPS is the paper-reported end-to-end inference speed, measured under the experimental setup described in the paper.
Model Checkpoint
- File:
model_final.pth - Backbone: ResNet-50
- Dataset: COCO 2017
- Framework: PyTorch and Detectron2
Code and Usage
The complete implementation, installation instructions, and evaluation commands are available on GitHub:
Citation
If you use this model in your research, please cite:
Fast Object Detection via One-Step Discrete Diffusion. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2027.
The official publication and BibTeX citation will be added when available.
License
Apache License 2.0.